Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Genome-wide analysis of mitochondrial DNA copy number reveals loci implicated in nucleotide metabolism, platelet activation, and megakaryocyte proliferation - R. J. Longchamps; S. Y. Yang; C. A. Castellani; W. Shi; J. Lane; M. L. Grove; T. M. Bartz; C. Sarnowski; C. Liu; K. Burrows; A. L. Guyatt; T. R. Gaunt; et al. (Human Genetics, 2022)
Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes ( p = 3.09 × 10 –15 ) and the gene ontology (GO) terms for mtDNA metabolism ( p = 1.43 × 10 –8 ) and mtDNA replication ( p = 1.2 × 10 –7 ). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall ( p = 0.044) and non-cancer mortality ( p = 6.56 × 10 –4 ).

A novel approach of homozygous haplotype sharing identifies candidate genes in autism spectrum disorder - Jillian P. Casey; Tiago Magalhaes; Judith M. Conroy; Regina Regan; Naisha Shah; Richard Anney; Denis C. Shields; Brett S. Abrahams; Joana Almeida; Elena Bacchelli; Anthony J. Bailey; Gillian Baird; et al. (Human Genetics, 2012)

Association of common DNA sequence variants at 33 genetic loci with blood lipids in individuals of African ancestry from Jamaica - R. Gupta; K. Ejebe; J. Butler; G. Lettre; H. Lyon; C. Guiducci; R. Wilks; F. Bennett; T. Forrester; B. Tayo; K. Musunuru; J. Hirschhorn; et al. (Human Genetics, 2010)